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1. Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers

   https://doi.org/10.1186/s12859-021-04427-7  

   Chrisman, Brianna S. ; Paskov, Kelley M. ; Stockham, Nate ; Jung, Jae-Yoon ; Varma, Maya ; Washington, Peter Y. ; Tataru, Christine ; Iwai, Shoko ; DeSantis, Todd Z. ; David, Maude ; et al ( October 2021 , BMC Bioinformatics)

   Sequencing partial 16S rRNA genes is a cost effective method for quantifying the microbial composition of an environment, such as the human gut. However, downstream analysis relies on binning reads into microbial groups by either considering each unique sequence as a different microbe, querying a database to get taxonomic labels from sequences, or clustering similar sequences together. However, these approaches do not fully capture evolutionary relationships between microbes, limiting the ability to identify differentially abundant groups of microbes between a diseased and control cohort. We present sequence-based biomarkers (SBBs), an aggregation method that groups and aggregates microbes using single variants and combinations of variants within their 16S sequences. We compare SBBs against other existing aggregation methods (OTU clustering andMicrophenoorDiTaxafeatures) in several benchmarking tasks: biomarker discovery via permutation test, biomarker discovery via linear discriminant analysis, and phenotype prediction power. We demonstrate the SBBs perform on-par or better than the state-of-the-art methods in biomarker discovery and phenotype prediction.

   On two independent datasets, SBBs identify differentially abundant groups of microbes with similar or higher statistical significance than existing methods in both a permutation-test-based analysis and using linear discriminant analysis effect size. . By grouping microbes by SBB, we can identify several differentially abundant microbial groups (FDR <.1) between children with autism and neurotypical controls in a set of 115 discordant siblings.Porphyromonadaceae,Ruminococcaceae, and an unnamed species ofBlastocystiswere significantly enriched in autism, whileVeillonellaceaewas significantly depleted. Likewise, aggregating microbes by SBB on a dataset of obese and lean twins, we find several significantly differentially abundant microbial groups (FDR<.1). We observedMegasphaeraandSutterellaceaehighly enriched in obesity, andPhocaeicolasignificantly depleted. SBBs also perform on bar with or better than existing aggregation methods as features in a phenotype prediction model, predicting the autism phenotype with an ROC-AUC score of .64 and the obesity phenotype with an ROC-AUC score of .84.

   SBBs provide a powerful method for aggregating microbes to perform differential abundance analysis as well as phenotype prediction. Our source code can be freely downloaded fromhttp://github.com/briannachrisman/16s_biomarkers.

    

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2. Shotgun metagenomics and microscopy indicate diverse cyanophytes, other bacteria, and microeukaryotes in the epimicrobiota of a northern Chilean wetland Nostoc (Cyanobacteria)

   https://doi.org/10.1111/jpy.13084  

   Satjarak, Anchittha ; Graham, Linda E. ; Piotrowski, Michael J. ; Trest, Marie T. ; Wilcox, Lee W. ; Cook, Martha E. ; Knack, Jennifer J. ; Arancibia‐Avila, Patricia ; Collier, ed., J. ( December 2020 , Journal of Phycology)

   ProkaryoticNostoc, one of the world's most conspicuous and widespread algal genera (similar to eukaryotic algae, plants, and animals) is known to support a microbiome that influences host ecological roles. Past taxonomic characterizations of surface microbiota (epimicrobiota) of free‐livingNostocsampled from freshwater systems employed 16S rRNA genes, typically amplicons. We compared taxa identified from 16S, 18S, 23S, and 28S rRNA gene sequences filtered from shotgun metagenomic sequence and used microscopy to illuminate epimicrobiota diversity forNostocsampled from a wetland in the northern Chilean Altiplano. Phylogenetic analysis and rRNA gene sequence abundance estimates indicated that the host was related toNostoc punctiformePCC 73102. Epimicrobiota were inferred to include 18 epicyanobacterial genera or uncultured taxa, six epieukaryotic algal genera, and 66 anoxygenic bacterial genera, all having average genomic coverage ≥90X. The epicyanobacteriaGeitlerinemia,Oscillatoria,Phormidium, and an uncultured taxon were detected only by 16S rRNA gene;GloeobacterandPseudanabaenawere detected using 16S and 23S; andPhormididesmis,Neosynechococcus,Symphothece,Aphanizomenon,Nodularia,Spirulina,Nodosilinea,Synechococcus,Cyanobium, andAnabaena(the latter corroborated by microscopy), plus two uncultured cyanobacterial taxa (JSC12, O77) were detected only by 23S rRNA gene sequences. Three chlamydomonad and two heterotrophic stramenopiles genera were inferred from 18S; the streptophyte green algaChaetosphaeridium globosumwas detected by microscopy and 28S rRNA genes, but not 18S rRNA genes. Overall, >60% of epimicrobial taxa were detected by markers other than 16S rRNA genes. Some algal taxa observed microscopically were not detected from sequence data. Results indicate that multiple taxonomic markers derived from metagenomic sequence data and microscopy increase epimicrobiota detection.

    

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3. Topiary: Pruning the manual labor from ancestral sequence reconstruction

   https://doi.org/10.1002/pro.4551  

   Orlandi, Kona N. ; Phillips, Sophia R. ; Sailer, Zachary R. ; Harman, Joseph L. ; Harms, Michael J. ( January 2023 , Protein Science)

   Ancestral sequence reconstruction (ASR) is a powerful tool to study the evolution of proteins and thus gain deep insight into the relationships among protein sequence, structure, and function. A major barrier to its broad use is the complexity of the task: it requires multiple software packages, complex file manipulations, and expert phylogenetic knowledge. Here we introducetopiary, a software pipeline that aims to overcome this barrier. To use topiary, users prepare a spreadsheet with a handful of sequences. Topiary then: (1) Infers the taxonomic scope for the ASR study and finds relevant sequences by BLAST; (2) Does taxonomically informed sequence quality control and redundancy reduction; (3) Constructs a multiple sequence alignment; (4) Generates a maximum‐likelihood gene tree; (5) Reconciles the gene tree to the species tree; (6) Reconstructs ancestral amino acid sequences; and (7) Determines branch supports. The pipeline returns annotated evolutionary trees, spreadsheets with sequences, and graphical summaries of ancestor quality. This is achieved by integrating modern phylogenetics software (Muscle5, RAxML‐NG, GeneRax, and PastML) with online databases (NCBI and the Open Tree of Life). In this paper, we introduce non‐expert readers to the steps required for ASR, describe the specific design choices made intopiary, provide a detailed protocol for users, and then validate the pipeline using datasets from a broad collection of protein families. Topiary is freely available for download:https://github.com/harmslab/topiary.

    

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4. DEPP: Deep Learning Enables Extending Species Trees using Single Genes

   https://doi.org/10.1093/sysbio/syac031  

   Jiang, Yueyu ; Balaban, Metin ; Zhu, Qiyun ; Mirarab, Siavash ; Solis-Lemus, ed., Claudia ( April 2022 , Systematic Biology)

   Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phylogeny. For example, they assume single-gene data (e.g., 16S rRNA amplicons) have evolved under the GTR model on a gene tree. Placement, however, often has a more ambitious goal: extending a (genome-wide) species tree given data from individual genes without knowing the evolutionary model. Addressing this challenging problem requires new directions. Here, we introduce Deep-learning Enabled Phylogenetic Placement (DEPP), an algorithm that learns to extend species trees using single genes without prespecified models. In simulations and on real data, we show that DEPP can match the accuracy of model-based methods without any prior knowledge of the model. We also show that DEPP can update the multilocus microbial tree-of-life with single genes with high accuracy. We further demonstrate that DEPP can combine 16S and metagenomic data onto a single tree, enabling community structure analyses that take advantage of both sources of data. [Deep learning; gene tree discordance; metagenomics; microbiome analyses; neural networks; phylogenetic placement.]

    

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5. An open-access T-BAS phylogeny for emerging Phytophthora species

   https://doi.org/10.1371/journal.pone.0283540  

   Coomber, Allison ; Saville, Amanda ; Carbone, Ignazio ; Ristaino, Jean Beagle ( April 2023 , PLOS ONE)

   Blair, Jaime E. (Ed.)

   Phytophthora species cause severe diseases on food, forest, and ornamental crops. Since the genus was described in 1876, it has expanded to comprise over 190 formally described species. There is a need for an open access phylogenetic tool that centralizes diverse streams of sequence data and metadata to facilitate research and identification of Phytophthora species. We used the Tree-Based Alignment Selector Toolkit (T-BAS) to develop a phylogeny of 192 formally described species and 33 informal taxa in the genus Phytophthora using sequences of eight nuclear genes. The phylogenetic tree was inferred using the RAxML maximum likelihood program. A search engine was also developed to identify microsatellite genotypes of P . infestans based on genetic distance to known lineages. The T-BAS tool provides a visualization framework allowing users to place unknown isolates on a curated phylogeny of all Phytophthora species. Critically, the tree can be updated in real-time as new species are described. The tool contains metadata including clade, host species, substrate, sexual characteristics, distribution, and reference literature, which can be visualized on the tree and downloaded for other uses. This phylogenetic resource will allow data sharing among research groups and the database will enable the global Phytophthora community to upload sequences and determine the phylogenetic placement of an isolate within the larger phylogeny and to download sequence data and metadata. The database will be curated by a community of Phytophthora researchers and housed on the T-BAS web portal in the Center for Integrated Fungal Research at NC State. The T-BAS web tool can be leveraged to create similar metadata enhanced phylogenies for other Oomycete, bacterial or fungal pathogens. 

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